DBSCAN Clustering explained | How DBSCAN clustering Works | Density based clustering

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DBSCAN Clustering explained | How DBSCAN clustering Works | Density based clustering
#DBSCANClustering #UnfoldDataScience

Hello ,
My name is Aman and I am a Data Scientist.

About this video:
In this video, I explain about DBSCAN clustering. I explain step by step process of DBSCAN clustering. I explain how density based clustering works. I explain how density based clustering works with example.
Below topics are explained in this video:
1. How DBSCAN clustering works
2. Density based clustering explanation
3. How density based clustering works step by step
4. What is epsilon in density based clustering
5. what is core point in DBSCAN clustering
6. What is border point in Density based cluster

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I'm doing Business analytics course and I refer to you video for understanding. Plz keep up the great work of enlightening us.

sumitjain
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Core Point:

A core point is a point that has enough neighboring points within a specified distance (called epsilon or eps).
Specifically, if a point has at least min_samples points (including itself) within a distance of eps, it is considered a core point.

Border Point:

A border point is a point that doesn't have enough neighboring points to be a core point, but it is within the eps distance of a core point.
Border points are on the edge of a cluster, but they are not dense enough to form their own core.

sangeethag
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Excellent Explanation!! Please upload more videos of this similar kind sir..

vallimuthaiyah
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Again nailed the topic. This is amazing how simply you have managed to explain the the concept

sumitjain
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your explanation is amazing man... keep going!

anifminhazkhan
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how to use DBSCAN in case of multiple features? Is there any technique to use only few features or all feature but less important with very small weightage?

sachinladdha
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Nice and sweet explanation. I shared with my friends. Thank you Aman

muhammedthayyib
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Thank you for the detailed explanation!

aiuslocutius
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Thank you so much. This is clear and on point. Subscribed!

luamalem
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HI
its very nice the way your explaing the topics really i love it thanks for the video

rds
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Please put something for deep learning like cnns rnns and examples for those

christygeorge
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Excellent explanation, but one question..how can we evaluate DBSCAN, is there any test like we evaluate k- means ckuster by silhouette test?

nareshjadhav
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If we give Epsilon=1 then it will randomly draw a circle on a particular data point and make its a circle with radius 1, so the core point is also chosen randomly

datascienceworld
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How to select the best algorithm for the data by looking at the data?
This the question that I faced in many interviews.
Can you please make a video on it?

navneetgupta
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Sir please upload a video on PCA next. 🙏

ranajaydas
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Hi sir, a great thanks from me. A general question sir, I have performed DBSCAN, Fuzzy, and K-means clustering, how would I suggest which algorithm is best for the data? If the dataset is quite mess, large scale 10k rows, and skewed with big amount of outliers

kar
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Can you pls make video on birch algorithm? Plz sir

surajgupta-dcue
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noise points are not consider in any clsuters right??? if new data is added, then that data points form a cluster around noise point and then that noise point is also includes in a cluster or not???.then accuary of algortm changes or remains constant???

ravanshyam
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Can you do a playlist on computer vision feature extraction techniques like hog sift (svm+hog), etc

austinmark
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Hi Aman,
Thanks for the explanation, but my doubt is how cluster can be decide which point needs to take as a core point? What is the math behind that?

nikhildesai